diff --git a/nodes.py b/nodes.py
index 23a4308..d3a2b5c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -243,10 +243,11 @@ class CreateFadeMask:
                  "start_level": ("FLOAT", {"default": 1.0,"min": 0.0, "max": 1.0, "step": 0.01}),
                  "midpoint_level": ("FLOAT", {"default": 0.5,"min": 0.0, "max": 1.0, "step": 0.01}),
                  "end_level": ("FLOAT", {"default": 0.0,"min": 0.0, "max": 1.0, "step": 0.01}),
+                 "midpoint_frame": ("INT", {"default": 0,"min": 0, "max": 4096, "step": 1}),
         },
     } 
     
-    def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level):
+    def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame=None):
         def ease_in(t):
             return t * t
 
@@ -259,32 +260,38 @@ class CreateFadeMask:
         batch_size = frames
         out = []
         image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
-        
+
+        if midpoint_frame is None:
+            midpoint_frame = batch_size // 2
+
         for i in range(batch_size):
-            t = i / (batch_size - 1)
-            
-            if interpolation == "ease_in":
-                t = ease_in(t)
-            elif interpolation == "ease_out":
-                t = ease_out(t)
-            elif interpolation == "ease_in_out":
-                t = ease_in_out(t)
-            
-            if midpoint_level is not None:
-                if t < 0.5:
-                    color = start_level - t * (start_level - midpoint_level) * 2
-                else:
-                    color = midpoint_level - (t - 0.5) * (midpoint_level - end_level) * 2
+            if i <= midpoint_frame:
+                t = i / midpoint_frame
+                if interpolation == "ease_in":
+                    t = ease_in(t)
+                elif interpolation == "ease_out":
+                    t = ease_out(t)
+                elif interpolation == "ease_in_out":
+                    t = ease_in_out(t)
+                color = start_level - t * (start_level - midpoint_level)
             else:
-                color = start_level - t * (start_level - end_level)
-            
+                t = (i - midpoint_frame) / (batch_size - midpoint_frame)
+                if interpolation == "ease_in":
+                    t = ease_in(t)
+                elif interpolation == "ease_out":
+                    t = ease_out(t)
+                elif interpolation == "ease_in_out":
+                    t = ease_in_out(t)
+                color = midpoint_level - t * (midpoint_level - end_level)
+
+            color = np.clip(color, 0, 255)
             image = np.full((height, width), color, dtype=np.float32)
             image_batch[i] = image
-            
+
         output = torch.from_numpy(image_batch)
         mask = output
         out.append(mask)
-        
+
         if invert:
             return (1.0 - torch.cat(out, dim=0),)
         return (torch.cat(out, dim=0),)